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Company operations and global trade necessitate storage. Warehouse management is essential for meeting customer expectations, ensuring product availability, and delivering globally swiftly and affordably. A company's international competitiveness can suffer from warehouse instability. This study assesses warehouse performance for competitive advantage. The report also explores top management's disregard for warehouse management's impact on manufacturing competitiveness. This paper reviews warehouse management system research from several industries. Reviewing literature for gaps and flaws. Quantitative methods include industrial specialized surveys. Three varied South African manufacturing enterprises will be case-studied. Deduction is used to analyze industry expert consultation data. Due to its accessibility and data collection suitability, convenience sampling was employed to choose participants. This study discovered many warehouse management issues in South African industrial firms. Integration and automation are lacking in current systems, hampering global competitiveness. Warehouse movements and infrastructure damage increased without zone selection. Technology-illiterate warehouse workers slowed operations. The study offers zone picking, warehouse training, and warehouse system integration and automation to address these difficulties. South African industrial firms should strengthen warehouse management systems, according to this study. These organizations can enhance operational efficiency, save money, and compete globally with the suggested changes. Zone picking, warehouse system integration, and worker technical training can alleviate challenges and provide companies an edge. This study highlights warehouse management's often overlooked significance in manufacturing competitiveness. International organizations can do better by studying warehouse management system problems and solutions. Practical warehouse management advice from this study increases South African industry competitiveness. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The automated storage and retrieval system (AS/RS) has been developed rapidly and has been widely used in various fields. To achieve the ultimate goal of improving the efficiency of warehouse operation, this article studies the problem of goods locationallocation on the premise of a comprehensive analysis of the efficiency of automated warehouses and the overall stability of shelves. The mathematical model is established. Traditional Tabu search algorithms depend on Tabu table construction to avoid multiple continuous cycles that may occur in the direct search process. In this article, the improved algorithm can directly search and store in the Tabu table to prevent the algorithm from infinite search. A complete solution in that Tabu list continues to be passed to act as a point solution to replace the iteratively update product until a set iteration interval of the product is completed. The machine learning system can get the optimal solution. The improved algorithm is applied to solve the model, and the running results and the performance of the algorithm itself are obtained. The improved Tabu Search algorithm has more advantages in the process of solving the model because it reduces the size of the whole problem. The algorithm also uses the penalty optimization function as a measure of each solution. The greater the penalty is, the worse the solution is, and the smaller the penalty is, the better the solution is. The operation process of the material in and out of the warehouse in one day is studied, and the validity of the optimization scheme of goods location allocation is verified by substituting specific data for the solution. Finally, the case of goods in and out of the warehouse is simulated by using simulation software, and the running state and efficiency of the stacker are displayed according to the data. The Tabu direct search table is combined with the structure and function of the principle of amnesty direct search to achieve the goal of direct search and decisionmaking. The equilibrium coefficient of its operation is calculated by using the data, which proves that the scheme is feasible and has practical significance and research value. [ABSTRACT FROM AUTHOR]

Background: With the continuing growth of warehouses globally, there is an increasing need for sustainable logistics solutions in warehousing, but research linking warehouse management systems (WMS) and sustainability is lacking. Methods: A systematic literature review and bibliometric analysis were conducted in Scopus and Web of Science databases from 2006 to 2022 to investigate academic knowledge of WMS contributing to warehouses' social and environmental sustainability. Results: Findings revealed only 12 topic-relevant articles from 2013 to 2022, primarily published recently. More recent articles have received more citations than earlier published works. The articles were from multiple research fields, such as business economics, engineering, computer science, and social sciences, with only one article on environmentally sustainable technologies. The top keywords were "warehouse management system", "internet of things", "industry 4.0" and "supply chain". Only six articles had environmental sustainability terms in the keywords. Findings show more discussions about social rather than environmental sustainability. Most studies suggest integrating WMS with other systems to support sustainability efforts in warehousing. Conclusions: The study addressed a gap in academic literature regarding WMS and sustainability. Research findings added knowledge of practical activities to achieve warehouse operations and performance sustainability and proactively reduce warehouse operations' environmental and social impacts. [ABSTRACT FROM AUTHOR]

Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field. [ABSTRACT FROM AUTHOR]

Most of the current warehouse management system is made out of two-dimensional (2D) plane schematic warehouse, which brings a lot of inconvenience to warehouse management, including the warehouse data, storage of goods, location search, inventory, etc. 3D warehouse model began to attract attention as it can provide more intuitive view of warehouse-related information. This paper aims to review and investigate the current key technologies used for 3D modeling of warehouse system. This paper reviewed the method of 3D view reconstruction of the warehouse management system, including the active and passive, active and passive fusion methods, and makes a detailed comparison between the active and passive methods. It was found that different methods were applied to reconstruct the 3D view of warehouse, each with its own advantages and disadvantages.

Augmented reality (AR) is a significant Fourth Industrial Revolution (IR4.0) technology that employs computer-generated display, sound, text, and effects to enhance the user's real-world experience via wearable devices. Order picking processes have had a substantial influence on overall operational efficiency in warehouse management systems (WMS). The conventional picking process is challenging to handle, which may result in deviations from the intended picking performance. Pick-by-vision, a new technological solution for order picking, is receiving growing attention and is now considered a significant WMS-supporting technology. This article explores the positive implications and prospects of utilizing AR pick-by-vision technology in the warehouse picking processes by performing a narrative review of the previous review articles. To demonstrate the focus of the main area, this study also presents the hierarchical classification structure of AR implementation in WMS and highlights the pick-by-vision method. The analysis provided important key findings by evaluating 23 articles (original articles and case studies) on AR pick-by-vision technology applications, which are significant to the prospective advantages of AR pick-by-vision deployment in warehouse operations. This study gathers knowledge and insight that can be used by both academics and professionals who are interested in optimizing this new advanced technology for future research. [ABSTRACT FROM AUTHOR]

Company X is a modern Indonesian retail company that faces stock inaccuracies continuously and the variances exceeds the limit set by the company that reduce its profits continuously. Variances are difficult to identify since the stock recording in company X is not separated between stock in the warehouse and the store and not using warehouse management system. Business process re-engineering will be carried out by proposed two alternatives using traceability technology for the warehouse management system. Standard operating procedures will also be developed and used as a guide in implementing the system. This study uses qualitative method from a semi-structured interview with the management team, observation, and focus group discussion. The data were analyzed with gap analysis, cause mapping, and analytical hierarchy process to select the proposed alternatives using QR Code and RFID. The results from data processing is show that the company select QR Code as the technology used. [ABSTRACT FROM AUTHOR]

Research purpose. The main objective of this work is to highlight the adoption of project management practices in companies’ digital transformation initiatives as a critical success factor. Thus, on the one hand, we will seek to systematize the importance of project management practices in the digital transformation of economic organizations and, on the other hand, analyze them based on a digital transformation project of one of the largest publishing groups in Portugal. Having previously studied the process of digital transformation of the warehouse through the adoption of a Warehouse Management System, it is now important to analyze to what extent the transformation process was successful and to what extent the management as a project contributed to the achievement of the intended objectives and generated economic value.

The integration of warehouse management systems with robotics and Evolutionary Intelligence (EVIN) technology is currently in the focus as a way to optimize warehouse operation and reduce assembly costs. This study proposes an EVIN-based solution towards warehouse management system design. A neural network-based analytical unit used in the system allows predicting the optimal number of robots in the warehouse. The proposed approach was evaluated by comparing the performance of systems with and without an analytical unit and ant colony optimization. In all cases, the use of the neural network gives the same amount of applications to be completed by fewer auxiliary robots in less time, and it results in reduction of the number of collisions during the movement of robots. The emergence of this structure improves navigation efficiency and allows reducing production maintenance costs. [ABSTRACT FROM AUTHOR]

Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field.

Background: With the continuing growth of warehouses globally, there is an increasing need for sustainable logistics solutions in warehousing, but research linking warehouse management systems (WMS) and sustainability is lacking. Methods: A systematic literature review and bibliometric analysis were conducted in Scopus and Web of Science databases from 2006 to 2022 to investigate academic knowledge of WMS contributing to warehouses’ social and environmental sustainability. Results: Findings revealed only 12 topic-relevant articles from 2013 to 2022, primarily published recently. More recent articles have received more citations than earlier published works. The articles were from multiple research fields, such as business economics, engineering, computer science, and social sciences, with only one article on environmentally sustainable technologies. The top keywords were “warehouse management system”, “internet of things”, “industry 4.0” and “supply chain”. Only six articles had environmental sustainability terms in the keywords. Findings show more discussions about social rather than environmental sustainability. Most studies suggest integrating WMS with other systems to support sustainability efforts in warehousing. Conclusions: The study addressed a gap in academic literature regarding WMS and sustainability. Research findings added knowledge of practical activities to achieve warehouse operations and performance sustainability and proactively reduce warehouse operations’ environmental and social impacts.

Research purpose. Through the adoption of the concept of the Proof of Concept, the main objective of this work is to highlight the approach that allows the framework and study of the viability of investments in the digital transformation of companies. The research focuses on the publishing sector and, mainly, on one of the largest publishing groups in Portugal and focuses on the strategic decision, due to the covid-19 pandemic situation, to adopt a Warehouse Management System to increase productivity, competitiveness, and sustainability of the company under study. Due to the need for confinement, publishers saw their sales drop drastically and the option of e-commerce implied the need for adjustments in the organizational dynamics associated with the distribution of products. The research/paper goal is to show the viability of investments in the digital transformation of companies in order to enlarge their efficiency and effectiveness.

Warehouse operations need to change due to the increasing complexity and variety of customer orders. The demand for real-time data and contextual information is requried because of the highly customised orders, which tend to be of small batch size but with high variety. Since the orders frequently change according to customer requirements, the synchronisation of purchase orders to support production to ensure on-time order fulfilment is of high importance. However, the inefficient and inaccurate order picking process has adverse effects on the order fulfilment. The objective of this paper is to propose an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using computational intelligence techniques to enable smart logistics for Industry 4.0. Based on the data collected from a case company, the proposed IoT-based WMS shows that the warehouse productivity, picking accuracy and efficiency can be improved and it is robust to order variability. [ABSTRACT FROM AUTHOR]

In this article, we describe the use of dynamic simulation when designing an effective system for order picking within a distribution warehouse. The simulation model was created in the Witness software environment for discrete dynamic simulation and is a modification of a general simulation model of material flows in supplier systems. Using the example of a batch system for picking orders in a drugstore goods warehouse, we discuss the possibilities of using a general simulation model of material flows as an effective framework for the development of system support for warehouse processes using WMS. The simulation model is based on the possibility of dividing any material flow in the supply system into a finite number of movements with the possibility of using one of the sources and fulfilment of certain conditions. In order to achieve the required optimisation of the order picking system, which depends, in particular, on the unknown duration of goods collection at the picking location, and on the duration of goods sorting in consolidation, the "what-if" analysis has been used as a tool to measure the impact of uncertainty of one or more variables entering the model on the uncertainty of output variables. The study showed that minimisation of the number of physical elements in the model leads to a significantly higher speed of its operation. By means of dynamic simulation, it is possible to test a large number of variants of the picking system layout in a relatively short time and minimise the risk of erroneous decisions associated with the implementation of a suitable WMS.

Industry 4.0 has a vital role in supply chain management as it can convert the supply chain to a smart one and make it more agile. One of the main pillars of Industry 4.0 is Internet of Things (IoT). Managing the warehouse the inventory is a very important part of managing the supply chain. In this research a system is proposed to implement the approach of Internet of Things in warehousing management by using Node-RED and MongoDB. The system illustrates how IoT can be implemented in a warehouse to gain benefits and to avoid the drawbacks of traditional warehouse management systems. For illustration, a dataset was used to show how IoT has a great impact on the warehouse operations, especially on the forecasting accuracy. It helps in providing real-time visibility of everything in the warehouse, increasing speed and efficiency, decreasing manual manpower, and preventing inventory shortage and counterfeiting. This research provides an effective roadmap for enterprises to improve their warehouses by using the Internet of Things.

The rapid development of the Internet of Things technology has laid the foundation for the automation and intelligence of warehouse management, and digital carrier technology is the core of establishing a smart warehouse logistics platform. In view of the problem of low efficiency and low accuracy of manual data collection in traditional warehouse management, this study takes the warehouse warehousing operations in the manufacturing workshop as the object, and designs an RFID-based warehouse management system including the management layer, the middle layer and the physical layer. The system can optimize the process of traditional warehousing links, dynamically and intelligently perceive management objects, and realize effective management and monitoring of warehouse management. The simulation model is established by Flexsim software to simulate the storage process, and the model entity is simulated in a short period of time, and the utilization rate of each equipment is analyzed, which provides a reference for the platform design to propose improvements and decision-making. [ABSTRACT FROM AUTHOR]

Currently, the execution of inventory tasks and order preparation within a warehouse are activities that have a high representation in the logistics costs of a company. Therefore, any decision made to increase productivity and decrease delivery time is needed. The execution of these operations is relevant to senior management and its implementation must be analyzed with a long-term financial sustainability of the strategy in mind. This study evaluates the economic impact of a warehouse management tool implementation (also known as WMS) in a company dedicated to the distribution of retail and wholesale hardware items. With the help of the dynamic systems tool, it is possible to evaluate the effect generated by the implementation of the project, considering the variables that directly and indirectly affect the cash flow of the company under study. Through the simulation carried out, it is possible to conclude that the warehouse management project has increase the company's cash flow in a period of five years. However, the results are only valid if senior management agrees to strengthen employee training processes and make a greater investment in technology that allows for faster and more efficient logistics processes. [ABSTRACT FROM AUTHOR]

With the hot rise and increasing popularity of e-commerce and online shopping, exponentially growing orders require to be picked up timely. In the order-based warehouse picking process, more intelligent optimization methods are urgently demanded to meet the maturity of robot technology. Due to the wide variety of SKUs (stock-keeping units) in warehouses, direct analysis based on orders can hardly get strong correlations among SKUs. Since orders and SKUs usually are presented in text form files, this paper proposes a clustering optimization algorithm based on text information granules which makes full use of context information and semantics. Storage assignment is determined by clustering SKUs first and then performing correlation analysis on the clustering results according to orders. The algorithm can give the semantic description of each cluster so that the clustering process is interpretable and transparent. Extensive experiments on a distribution center of a clothing production enterprise indicate 40.5 to 57.9% improvement in the average order picking distance compared with the ABC classification.

The aim of this article is to present the results of development work on DataConnect system using the Universal Data Exchange Interface (UDEI) for the information exchange model between ExpertWMS® and external ERP systems. The development work was the final step of research conducted to the resistant on electromagnetic disturbances system based on the selective RFID gate technology. The article presents both a theoretical and practical approach to data exchange and communication in industrial enterprises using complex IT systems to support production, logistics and business processes.

This study presents two approaches to the prevention of inter-robot collisions. The first approach is to develop trajectory planning and motion control algorithms. The second approach is to reduce the number of auxiliary robots as much as possible. The rigidly programmed systems are less flexible and adaptive than systems capable of independent data analysis and pattern identification. Therefore, this study uses the neural network for robot training and an analytical module (AN) to make decisions regarding the quantity of robots. The AN assisted and non-assisted management systems were examined under the two scenarios, namely the steady and random increment of applications. In both scenarios, using the AN reduced the number of auxiliary robots and, consequently, robot collisions in the operating area. This can help to reduce the warehouse maintenance costs and improve manufacturing scalability. Therefore, the proposed robotic management system has the potential to enhance warehouse efficiency. [ABSTRACT FROM AUTHOR]

For the purpose of timely response to requests of any participant in the supply chain, as an integral part of every logistics system, warehouse can be found at any place in the supply chain, between suppliers and customers. In that sense, warehouse management involves the control and optimisation of the complex warehouse and distribution system. It might be said that warehousing and inventory management represent support to the production process and strive to complete coordination in relations with all functions, such as marketing, finance, human resources etc. Therefore, any disruption in coordination can cause serious problems throughout the whole business process. When there is a need for achieving operational efficiency and cost savings, warehouse management and warehouse operations are appropriate areas, in terms of achieving savings which will not jeopardize the quality of products and services. Especially, order-picking, as part of the warehousing process, is one of the most important activities when it is about providing savings. Order-picking process involves taking raw materials/products from a specific location in the warehouse, so as to respond to requirements of production and/or customers. It is estimated that the costs of order-picking participate with 55% in the total cost of warehousing. However, elimination of this activity can increase the level of dissatisfaction of partners in the supply chain, and, in that way, increase the cost of lost sales. In this regard, one should not consider elimination of order-picking from the warehousing process as an option, but rather find a way to increase its efficiency. Due to their flexibility in the order-picking process, people cannot be fully replaced by machines and technologies. However, equipment of order-picking process by the adequate technology could increase efficiency of process and productivity of employees in the warehouse. In that sense, warehouse management system (WMS) is an information technology whose implementation has the aim to increase efficiency of processes performed in warehouse. Therefore, the aim of the paper is to emphasize the importance of implementing a warehouse management system for improving the order-picking process, as warehouse activity. In order to accomplish this aim, empirical research has been conducted. A random sample of companies specialized for performing distribution activities has been chosen. The authors of the paper have analysed which segment of order-picking process can achieve maximum benefits from implementation of this technology, but also whether there are any limitations in terms of implementation of WMS. Based on the statistical methods (descriptive statistics and cluster analyses), through the SPSS software package, the results presented in the paper indicate the segments of order-picking process which are mostly improved by implementing the WMS, as information technology.

This study has been done to create a system and standard to control the warehouse and also make it easier to find tools. The warehouse is full and finding tools is difficult because there is no regulation to control how the warehouse works. This research uses direct observation in the field, and interviews with the department related to supporting tool warehouses. The results of the observations is the organization of a new management system in the form of Standard Operating Procedure (SOP), rules, and a new layout for tools. Based on the existing SOP, stock cards and material, a parent table is made for controlling tools in the warehouse. Controlling is carried out with aim that the warehouse works according to its SOP and eliminates current problems.

Warehouse is a place used to store and put items owned by company. There are many activities carried out in the warehouse, starting from data collection process to management of the items. However, there are still companies that have difficulty in collecting data and management of available items. As the process of collecting goods is still done manually, it takes a long time to find information from the item. Therefore, a Warehouse Management System (WMS) was built by implementing the ABC classification method to help the company in managing items at the warehouse. ABC classification method is a method used to group items into certain classes based on the annual demand of the item. This method is used to regulate the placement of items in the warehouse. After the ABC method is implemented on the system, the company is able to to arrange the items and to obtain the information of the most needed items by consumers easier. The result of User Acceptance Test (UAT), which was conducted 3 times, states that all of user needs have been fulfilled and accepted entirely. Then, based on the results of the questionnaire recapitulation distributed to employees as many as 30 respondents, it is obtained that as many as 91.33% of respondents state that the system is easy to use and provides complete and accurate information (strongly agree).

Business logistics in 21st century has been transforming its form, because it could be characterised by higher flexibility, innovations, accuracy, reliability, orientation on customer and application of worldwide logistic technologies and systems. Modern trends and logistic direction are mutually devoted just very little attention. The main aim of the paper is to identify the impact of selected information technologies on selected logistics processes. As a result of digitization, logistics forms the basis for the implementation of new modern technologies, the transition to advanced production systems and the differentiation of product offerings. This is also confirmed by the results of a survey conducted in 85 Slovak enterprises and a statistical evaluation of the survey. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Industry 4.0 technologies embedded in the warehouse management system (WMS) are needed to improve the automation of material handling activities such as receiving, storing, picking, sorting, packaging, and delivering. This research aims to introduce a neutrosophic multi-criteria group decision-making tool that is intelligible in supporting the transition and upgrading of WMS with Industry 4.0-based solutions. This advanced two-stage model is based on the integration of the logarithmic percentage change-driven objective weighting (LOPCOW) method and the additive ratio assessment (ARAS) method under the type-2 neutrosophic number (T2NN) environment. In the first stage, T2NN-LOPCOW generates an objective importance vector of decision-making criteria. In the second stage, T2NN-ARAS based on the generalized weighted Heronian mean operator provides an advantageous order of Industry 4.0-based material handling technologies. T2NN-LOPCOW-ARAS brings the following novelties: ((i) to straightforwardly represent and explore interconnection levels between weights of criteria, ((ii) to provide wide-scoping insight into the stability of initial priority order, as well as a broad spectrum of flexible solutions, ((iii) to control the normalization procedure and minimize distortions due to the double-normalization backbone. The real-life case study of a logistics company from the Serbian grocery retail sector illustrates the practical applicability of T2NN-LOPCOW-ARAS. A practical evaluation framework is defined to comprehensively assess automated guided vehicles (AGVs), collaborative robotics, and drones. The sensitivity analyses show the high robustness of the proposed framework. The comparative investigation shows that T2NN-LOPCOW-ARAS is superior to the extant methods. The research findings show that AGVs are the most favorable Industry 4.0-based material handling solution. • Material handling technology selection in smart and sustainable WMS is addressed. • Advanced two-stage LOPCOW and ARAS-based type-2 neutrosophic model is introduced. • Proposed intelligible MCGDM tool can support the transition to smart warehousing. • The comparative investigation demonstrates the superiority of T2NN-LOPCOW-ARAS. • Managerial insights for selecting an Industry 4.0-based material handling technology. [ABSTRACT FROM AUTHOR]